Internal SPLS functions.
heatmap.spls( mat, coln=16, as='n', ... )
spls.dv( Z, eta, kappa, eps, maxstep )
ust( b, eta )
correctp( x, y, eta, K, kappa, select, fit )
cv.split( y, fold )
wpls( x, y, V, K=ncol(x), type="pls1",
center.x=TRUE, scale.x=FALSE )
sgpls.binary( x, y, K, eta, scale.x=TRUE,
eps=1e-5, denom.eps=1e-20, zero.eps=1e-5, maxstep=100,
br=TRUE, ftype='iden' )
sgpls.multi( x, y, K, eta, scale.x=TRUE,
eps=1e-5, denom.eps=1e-20, zero.eps=1e-5, maxstep=100,
br=TRUE, ftype='iden' )
cv.sgpls.binary( x, y, fold=10, K, eta, scale.x=TRUE, plot.it=TRUE,
br=TRUE, ftype='iden', n.core=8 )
cv.sgpls.multi( x, y, fold=10, K, eta, scale.x=TRUE, plot.it=TRUE,
br=TRUE, ftype='iden', n.core=8 )These are not to be called by the user.
Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.
Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3--25.